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Field
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. You will: Develop and apply novel graph learning based methods for predicting the synthesisability of organic molecules Develop and apply novel neurosymbolic AI methods to chemical problems If appointed
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to students pursuing degrees through the doctoral level. More than 20 percent of its 25,000 students are enrolled in graduate course work, studying in disciplines ranging from atomic physics and graph theory
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main areas of research are machine learning, distributed systems, and the theory of networks. Within these three areas, we are currently working on several projects: graph neural networks, natural
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degrees through the doctoral level. More than 20 percent of its 25,000 students are enrolled in graduate course work, studying in disciplines ranging from atomic physics and graph theory to medieval
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: o Quantitative analysis, e.g. descriptive statistics from survey data, creating basic data visuals such as bar graphs using Excel or other software, OR o Qualitative analysis, e.g. coding
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are looking for strong candidates who would complement and/or strengthen existing research areas in our department (which currently includes discrete mathematics, optimization, graph theory, algorithms
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combinatorics, discrete and computational geometry, extremal combinatorics, graph and hypergraph theory, probabilistic combinatorics, and their applications to data science, network science, and computer
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is like Ockham's razor, seeking a simple theory that fits the data well. It can also be thought of as file compression - where data has structure, it is more likely to compress, and the greater
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: Statistical analysis & causal inference Data management, collection & visualization Social network analysis & graph theory Online tool/web development Running controlled experiments Game-theoretic modelling
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, Computational Social Science, Big Data. Relevant skills could include statistical analysis, data management and collection, causal inference, network analysis, graph theory, visualizations, and online tool